Few Shot Learning

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• Transfer Learning
• Multitask Learning
• Understanding few-shot learning in machine learning | Michael J. Garbade

Advances in few-shot learning: a guided tour | Oscar Knagg

• Advances in few-shot learning: reproducing results in PyTorch | Oscar Knagg- Towards Data Science

• Matching Networks: A differentiable nearest-neighbors classifier
• Prototypical Networks: Learning prototypical representations
• Model-agnostic Meta-Learning: Learning to fine-tune

N-shot, k-way classification tasks

The ability of a algorithm to perform few-shot learning is typically measured by its performance on n-shot, k-way tasks. These are run as follows:

1. A model is given a query sample belonging to a new, previously unseen class
2. It is also given a support set, S, consisting of n examples each from k different unseen classes
3. The algorithm then has to determine which of the support set classes the query sample belongs to

Matching Networks

combine both embedding and classification to form an end-to-end differentiable nearest neighbors classifier.

1. Embed a high dimensional sample into a low dimensional space
2. Perform a generalized form of nearest-neighbors classification

The meaning of this is that the prediction of the model, y^, is the weighted sum of the labels, y_i, of the support set, where the weights are a pairwise similarity function, a(x^, x_i), between the query example, x^, and a support set samples, x_i. The labels y_i in this equation are one-hot encoded label vectors.

Matching Networks are end-to-end differentiable provided the attention function a(x^, x_i) is differentiable.